Modeling Opinion-Behavior Co-evolution for Explaining Opinion Polarization: A Framework
A standalone scholarly copy of Chapter 5 of Tanzhe Tang's 2022 doctoral dissertation.
Thesis chapter / manuscript version · Delft University of Technology · 2022
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Abstract
The dynamics of opinions and behaviors are almost inseparable. Therefore, modeling opinion-behavior co-evolution is expected to be a more realistic approach to explaining opinion polarization than modeling opinion dynamics alone. Regardless of the co-evolution models that have already been proposed in various disciplines, this novel approach is still largely uncoordinated, and a unifying framework is in urgent need to organize existing efforts and facilitate future studies. In this paper, such a framework, called MOBEP (Modeling Opinion-Behavior Co-evolution for Explaining Opinion Polarization), is developed based on the central notion that behaviors serve as the messenger of opinions, highlighting the inference process that translates observed behaviors into inferred opinions. For validation purposes, the framework is applied to a selection of representative models. Finally, a case study about mask wearing during the COVID-19 pandemic is presented as a vivid demonstration of how the framework works.
Why this chapter is highlighted
This is the dissertation’s integrative framework contribution. It introduces MOBEP to connect opinion dynamics with observable behavior, organizes modeling approaches spread across several disciplines, and shows how the framework can be used in a case study of mask wearing. Unlike the article-based chapters represented separately on the publications page, this work was prepared as a standalone manuscript but remained available only as Chapter 5 of the dissertation.
How to cite
Tang, T., Ghorbani, A., & Chorus, C. G. (2022). Modeling opinion-behavior co-evolution for explaining opinion polarization: A framework. In T. Tang, On the Methods for Explaining Polarization of Private and Unobservable Opinions: An Opinion-Behavior Co-Evolutionary Approach (Chapter 5, pp. 111–154). Delft University of Technology.
This page provides an independently citable copy of a chapter in the 2022 dissertation. It is not presented as a separate peer-reviewed journal publication. The standalone PDF reproduces the final published chapter and adds only a bibliographic cover page. The dissertation is available under a CC BY license through the TU Delft repository.